Gall's Legacy Revisited

Decomposition and Localization in Cognitive Neuroscience

Tadeusz Zawidzki and William Bechtel

Philosophy-Neuroscience-Psychology Program

Washington University in St. Louis

Explanations of activities of living systems in the life sciences have
often taken the form of mechanistic explanations in which the activity
of the system is identified with a component of the system. The overall
activity is decomposed into sub-functions, which are then localized within
the responsible component (Bechtel and Richardson, 1993). Mental activities
have been no exception. The brain (including the central nervous system)
has long been taken to be the responsible organ of the body for mental
activities, and the task has been to decompose mental functions and identify
the resulting component functions with parts of the brain. In many respects,
this effort was the product of adopting the vision of Franz Joseph Gall.

The pursuit of mechanistic explanations via decomposition and localization
has always been controversial, both for biological functions generally
and mental activities in particular. In biology the opposition has generally
been termed vitalism. While vitalists such as Bichat and Pasteur
were often right in rejecting the overly simplistic mechanistic accounts
of their contemporaries, they themselves generally failed to advance the
effort to explain functions since they offered little in the way of a positive
methodology. Much the same has been true with mental activities. Some of
the opponents of mechanism (e.g., Flourens) have been overtly dualists
and have denied that knowledge of the brain could contribute to the development
of explanations of mental activities. Others (e.g., Lashley) have acknowledged
that the brain was responsible for mental activities, but simply resisted
any attempt to differentiate cognitive functions across brain structures.
Some contemporary advocates of dynamical systems theory seem to fall in
the latter camp (van Gelder, van Orden), although, unlike early opponents
of mechanistic explanation, these theorists do offer an alternative strategy.
We shall, at the end, argue that their opposition to decomposition and
localization is misguided.

Despite the challenge of dynamical systems theory, modern cognitive
neuroscience, the most sophisticated attempt yet to link mental functions
with neural structures, has embraced the quest for mechanistic explanation
through decomposition and localization. Thus, part of the legacy of Gall
is very much still alive. However, there are different strands in this
legacy. Part of our goal is to differentiate three of these strands. One,
which we link with Fodor's proposal for modularity, follows Gall himself
in seeking direct neural instantiation of mental functions. A second strand,
more prevalent in philosophical accounts of mind-brain relations than actual
empirical inquiries, but implicit in psychological research which is cut
off from neuroscience, recognizes the need to localize functions at a finer
grain, but adopts a top-down perspective in which decomposition precedes
localization. The third construes decomposition and localization as each
informing the other. One element of this view is the claim that brain regions
are not independently demarcated, but are in part identified through the
process of assigning functions to them. The second element, and the one
to which we will devote primary attention, is that information gleaned
from neural studies of component structures often leads to fundamental
revisions in functional decompositions of cognitive performance. It is
this (neo) Gallean strand that we see as most common in cognitive neuroscience,
and which we will be principally concerned to explicate through examples.

1. Gall and the Program of Direct Localization

Gall is widely known for his proposal that mental faculties are localized
on the surface of the brain. Gall was not, however, the first to propose
that regions of the brain had distinctive mental functions. Several investigators
in the 18th century had proposed localization of either sensory motor functions
or of psychological capacities. However, the legacy of Albrecht von Haller,
who contended that all neural tissue was highly irritable, and thus provided
a sensorium commune, and who rejected any attempt at localization
of specific functions in the brain, dominated the research landscape in
the 18th and early 19th century (see Clarke and Jacyna, 1987).

While Gall's organology (more widely known as phrenology, the
term supplied by his collaborator Spurzheim in 1818) is largely ridiculed,
in part because of the quackery the surrounded its practices, it was based
on a number of assumptions, some plausible but mistaken, some more radical
but ultimately more influential. One of the mistaken assumptions was that
the skull fit closely to the brain so that the shape of the skull provided
a reliable indicator of the shape of the brain. Another assumption, which
has earlier roots and enduring legacy, was that there were distinct mental
capacities or faculties and that individuals differ in these faculties.
These faculties characterize the different abilities or propensities to
action, upon which individuals might differ, such as verbal memory (the
first ability Gall focused on), destructiveness, and physical love. Gall
identified 27 such faculties: Spurzheim and others later expanded his list.
Assumptions like this persist today in such forms as Chomsky's claim that
there is a language faculty (Chomsky, 1980, Pinker, 1994). Yet a third
assumption, and a radical one given von Haller's influence, was that these
faculties were localized in distinct parts of the brain. Fourth was the
assumption that greater development in a localized brain area corresponded
with larger size. This assumption, as plausible as it might have seemed,
has been discredited. But it has been replaced with a conceptually related
assumption that greater activity in a brain area corresponds with greater
metabolism (e.g., blood flow).

Figure 1. From Spurzheim (1834).

Given these assumptions, the motivation for seeking correlations between
skull shapes and distinct psychological faculties, and using this to infer
the function of underlying brain structures seems well motivated. The actual
linking of particular faculties with skull shapes was an empirical enterprise,
and here Gall's work is most suspect. Gall tended to focus on examples
of individuals with special development or deficit in particular faculties
and protrusions or indentations in parts of their cranium that corresponded,
in his scheme, to these faculties. From these cases he advanced his famous
maps of functions (Figure 1). But what is more important for us is the
general view he defended, in which specific brain regions were responsible
for specific psychological capacities:

When we see that nature follows such a course, how can it still be doubted
that each part of the brain has different functions to fulfill, and that
as a consequence, the brain of man and animals must be composed of as many
special organs as the man or animal has distinct moral or intellectual
faculties, inclinations, and aptitudes for work (Gall and Spurzheim, 1812,
p. 254).

Gall's legacy was invoked by many neuroscientists later in the 19th
century as they began to identify regions of the brain with specific mental
activities. These theorists sharply distinguished Gall's assumptions about
how the cranium mapped underlying brain structure from his contention that
different brain areas carried out different mental processes. They then
developed new tools, such as linking deficits to lesions or direct electrical
stimulation of the brain, to localize functions in the brain. But by the
20th century, Gall came to be held in far lower repute until he was "rehabilitated"
by Fodor (1983). Fodor contended that a good part of the mind had the character
of Gall's faculties; he identified these areas as modules and claimed that
they were responsible for processing particular contents. But Fodor went
far beyond Gall in emphasizing the encapsulation of these modules--each
module had to rely on its own source of information and its operation could
not be penetrated by other information the subject possessed. Fodor's account
departs from Gall's in that he only identified such modules in specific
sensory input domains and in language processing and he viewed these modules
not as directly controlling particular behaviors, but as supplying input
to more central cognitive processes which are responsible for higher level
reasoning and operate in a more holistic manner. One kind of information
that cannot penetrate the input modules, then, is information held by central
cognition. Fodor also identified a number of other characteristics of information
processing within modules: such processing is very fast, automatic, and
mandatory (when the input is received, it is processed). He also contended
that such modules are likely to be hardwired into the cognitive system.
Accordingly, they would presumably be systems that are physically identifiable
in the brain, although Fodor does not emphasize the physical localization.

One important point to note about both Gall and Fodor is that for both
of them the analysis of function comes prior to the analysis of structure.
Gall (using "physiology" to refer to function) wrote:

The physiology of the brain need not be contradictory to the anatomy
of the brain; its anatomy must become the support of its physiology. But
physiology has been found independently of anatomy. Nothing whatever in
brain physiology has conflicted with an anatomical fact; nothing has been
interpreted by structure or by the arrangement of parts of the brain; brain
anatomy has served only to confirm physiological discoveries. (1825, p.
118)

For Fodor, likewise, functions are identified autonomously from their
brain realization. The primary criteria for identifying modules are their
functions--each module is specific to a given sense or to language.

2. Top-Down Complex Localization

In linking his modularity thesis to Gall's organology, Fodor characterizes
the faculties or modules as "vertical" and distinguishes them
from another sort that are common in cognitive theories, which he calls
"horizontal." The difference lies in the fact that Gall's and
Fodor's faculties are responsible for a given content domain whereas horizontal
faculties operate across content domains. Short-term memory, if there turns
out to be such a system, stores information on a variety of contents. Moreover,
in carrying out any given task, it is assumed to interact with other cognitive
capacities. In Fodor's perspective, these horizontal faculties belong to
central cognition, and he is deeply pessimistic about the ability of cognitive
science to make any progress in explaining these capacities. Cognitive
science nonetheless has generally taken these horizontal faculties to be
its principal focus and has tried to develop a number of tools for demonstrating
the existence of particular processing components and determining how they
interact with each other. (And, as we shall see, cognitive neuroscience
offers consolation to the worry that central cognition cannot be mechanistically
explained.)

Identifying horizontal modules requires going beyond simple localization;
it requires decomposing a given task into subtasks. The need for decomposition
in order to explain is independent of whether an initial direct localization
is correct. If it is, then the decomposition occurs within the module.
But a common fate of proposed direct localizations in the life sciences,
including cognitive neuroscience, is that the structure in which a function
is first localized turns out to be only one contributor to the performance
of the task. For example, shortly after Broca (1861) identified a region
in the brain as the source of language deficits and thus as potentially
responsible for articulate language, Wernicke (1874) identified a second
brain region that also produced language deficits, although of a different
sort, and proposed a model in which different areas of the brain contributed
in different ways to language functioning. Such a differentiation of tasks
is a step toward developing a functional decomposition of an overall function
into sub-tasks or component functions, which then together accomplish the
activity. It is these functions which a more adequate explanatory account
will hope to localize in the brain. A central methodological question concerns
how these component functions are to be identified. In Wernicke's case
(and in the case of Geschwind, 1974, who carried on Wernicke's tradition),
brain lesions have been the primary guide to functional decomposition.
But brain lesions have played only a minor role in the decompositions developed
in cognitive science.

One approach is to think that functional decompositions can be developed
a priori by asking what activities must be performed in order to perform
the overall task. Such an approach is common in engineering: one designs
a machine to carry out a task by first identifying component tasks, then
identifying component tasks within these tasks, and so on, until one reaches
a point where the tasks invoked in the analysis correspond to the capabilities
of known mechanical devices. Then one puts the devices together in such
manner as to interact appropriately to perform the task. This approach
is exemplified in designing computer programs: the programmer must analyze
the overall performance that is sought into component activities until
these activities correspond to the primitive operations of one's programming
language or to already developed programming modules. Such an approach
has been followed by many investigators in artificial intelligence. A common
result is that initial proposals as to how a given task should be decomposed
turn out to be inadequate to account for the way the task is usually performed,
and researchers need to develop alternative models, including new modeling
tools (e.g., scripts and frames) and new modeling frameworks (e.g., production
systems and case based reasoning systems). In all these cases, though,
the guide to developing new tools and frameworks has come from considerations
of the task that needs to be performed (e.g., making a medical diagnosis
or reading a newspaper story) and means-end reasoning about that task.

Another approach to identifying functional components is through behavioral
experimentation. The goal here is to find evidence in the behavior of the
mind/brain about how it breaks down tasks. The challenge in such research
is to find a variable to measure that might point to the differentiation
of tasks. Two of the most widely invoked are reaction times and error measures.
In reaction time studies one measures the amount of time it takes one to
perform a given task. The problem is that one must measure a whole activity,
which involves sensory processing, whatever central activities are involved,
and motor processing. To get at the amount of time involved in a specific
cognitive task, one generally has to do comparative studies. One of the
first approaches to using reaction times to identify cognitive processes
was Donders' (1868/1969) use of the subtraction method in which one devised
two tasks in which one could be envisaged as adding an additional step
to what was required in the other task. The time difference in the performance
of the two tasks was then a measure of how long the additional task required.
There are at least two problems with this approach. First, it requires
a proposal for a task decomposition before one can set up the substraction.
Second, it assumes that performing the additional task simply adds to the
other operations and does not occur simultaneously with them. Saul Sternberg
(1967) developed a procedure, known as the additive factors method,
to overcome this later problem. In this procedure one devises procedures
to alter different variables that are presumed to affect different component
tasks. If the changes in reaction times resulting from manipulating two
variables are additive, then the affected component tasks are revealed
to be independent, even if they occur simultaneously. If, on the other
hand, the change in reaction time from manipulating two variables is not
equal to the sum of changes for manipulating the variables independently
(this is called an interaction), then the component tasks are not
independent. A related way of demonstrating whether two components are
independent is the interference paradigm. If requiring a subject to perform
a second task while performing an initial task leads to increased reaction
times, this provides evidence that a common component is employed in both
tasks.

While all of these methods begin with an hypothesis about the decomposition
of the task, it should be emphasized that the additive factors method and
the interference paradigm provide a means for possibly falsifying the initial
hypothesis and so guiding the quest for a more adequate decomposition.
What is important for our purposes, though, is to note that they start
on the behavioral side, using behavioral data to test and revise task decompositions.
The task decomposition is set, therefore, before one might begin to consider
neural evidence.

So far we have focused on top-down decomposition in artificial intelligence
and cognitive psychology. But the program has also been endorsed in philosophy
of mind. The dominant philosophical account of mind of the past 30 years
is functionalism, which holds that mental activities are to be understood
in terms of their interaction with other mental activities, not their physical
realization. One strand of functionalism, homuncular functionalism, advocates
a multi-stage decomposition of cognitive performance, usually first framed
in intentional terms, but eventually reaching a point at which a task is
sufficiently simple that one can envisage a mechanistic system performing
it. Dennett characterizes this as the approach of AI researchers:

The AI researcher starts with an intentionally characterized
problem (e.g., how can I get a computer to understand questions
of English?), breaks it down into sub-problems that are also intentionally
characterized (e.g., how do I get the computer to recognize questions,
distinguish subjects from predicates, ignore irrelevant parsings?)
and then breaks these problems down still further until he reaches problem
or task descriptions that are obviously mechanistic (1978, p. 80).

One stream of functionalism takes the emphasis on function to show that
reduction of psychological states to brain states is not possible, largely
because psychological states can be realized in very different types of
brain states (Putnam, 1975, Fodor, 1987). Accordingly, that strand of functionalism
often denies that knowledge of the brain can be informative of psychological
processes. Homuncular functionalism, as Lycan (1981) makes clear, does
not endorse such a chasm between mind and brain, and indeed is compatible
with the classical theory of the identity of mind and brain. Closely identified
with the identity theory is a model of reduction, according to which psychological
processes are identified with neural processes and psychological laws are
derived from laws of neuroscience. The resulting picture is nicely described
by Chalmers:

The paradigm of reductive explanation via functional analysis works
beautifully in most areas of cognitive science, at least in principle .
. . most nonphenomenal mental concepts can be analyzed functionally. Psychological
states are characterizable in terms of the causal role they play. To explain
these states, we explain how the relevant causation is performed . . .
In principle, one can do this by giving an account of the underlying neurophysiology.
If we explain how certain neurophysiological states are responsible for
the performance of the functions in question, then we have explained the
psychological state. We need not always descend to the neurophysiological
level, however. We can frequently explain some aspect of mentality by exhibiting
an appropriate cognitive model - that is, by exhibiting the details of
the abstract causal organization of a system whose mechanisms are sufficient
to perform the relevant functions, without specifying the physicochemical
substrate in which this causal organization is implemented. In this way,
we give a how-possibly explanation of a given aspect of psychology, in
that we have shown how the appropriate causal mechanisms might support
the relevant mental processes. If we are interested in explaining the mental
states of an actual organism or type of organism (e.g., learning in humans,
as opposed to the possibility of learning in general), this sort of explanation
must be supplemented with a demonstration that the causal organization
of the model mirrors the causal organization of the organism in question
(Chalmers 1996, p.46).

The approach of AI, cognitive psychology, and homuncular functionalism
in philosophy that we have characterized in this section emphasizes a top-down
direction to the development of mechanistic theories of mind: the decomposition
is first developed by analyzing behavior, and perhaps subsequently connected
to underlying neural processes. There are some vocal opponents of the picture
of functional decomposition first, structural localization second, such
as Paul and Patricia Churchland. In their extreme eliminativist mode, they
recommend ignoring the functional decomposition of cognitive capacities
in favor of an approach that focuses solely on the brain. (As McCauley,
1996, argues, based on a number of texts, this is not the only position
that the Churchlands adopt. In other contexts, they embrace the sort of
interactive approach that we discuss in the next section.) Insofar as they
endorse eliminating not just folk psychology, but also the functional analyses
of cognitive psychology and cognitive science, when reduction fails, they
seem to endorse the view that if such functional analyses are to play any
role in a proper science, it must be that of providing a prior decomposition
that is to be completed before neuroscience takes over to localize functions.
The eliminativist claim is that neuroscience won't find any components
carrying out these functions, and that the whole functional analysis will
be eliminated.

3. Interactive Decomposition and Localization

Cognitive neuroscience, both in its late 19th and early 20th century
development, and in its current pursuit, supports a different perspective
on the functional decomposition and neural localization of mental function.
The identification of neural structures, the very 'carving' of the brain,
does not proceed independently of an assumed functional decomposition of
cognitive capacity and the functional decomposition of cognitive capacity
is informed by neuroanatomical evidence (Mundale and Bechtel, 1996). These
features of the methodology of contemporary cognitive neuroscience, exemplified
in the cases we discuss, set it apart from the Gallean direct localizationist
tradition and the top-down approach to reductionism found in much contemporary
philosophy of mind. The complex interplay between cognitive, functional
decomposition and neural localization that we highlight suggests that the
study of complex systems like the brain is an inherently inter-disciplinary
endeavor, involving the close cooperation of many different theoretical
perspectives, rather than their independence in a strict division of labor.
To illustrate the nature of this dynamic interaction, we will analyze two
lines of research in contemporary cognitive neuroscience. The first began
with work on visual processing in monkey brains that pointed to a rather
surprising decomposition between two visual processing systems, one primarily
concerned with identifying what an object is and the other with determining
where it is. As we shall see, once this decomposition was suggested from
research on the brain, it has led to new threads of research on the cognitive
side, both in terms of computer modeling of cognition and behavioral research.
The second line of research examines developments in the study of attention.
Here again information stemming from the brain has prompted revisions in
the decomposition of cognitive function.

a. Decomposition and Localization in the Visual Processing System

The phenomenology of seeing is one of direct access to nearly all aspects
of the visual field. We are not aware of this task being decomposed into
subtasks. But when one asks how to get a machine, biological or silicon
based, to do it, it is immediately obvious that this is not a simple, direct
process. We are not aware of any physical process that directly transforms
sensory input into a perception of the world in which one recognizes objects
and their significance, where they are, and how they are moving. Since
perceiving is not a simple physical process, if we are to understand it
or replicate it, we need to decompose it into subtasks that are more directly
physically realizable. This task has been taken up in a practical way in
computer vision. Here one starts with a characterization of the nature
of the information received at the sensory threshold [1]
and then tries to devise algorithms which operate on this information until
a perception of a three dimensional world is built up. One of the more
celebrated efforts of this sort is that of Marr (1982). This type of approach
decomposes the process in a nearly totally top-down fashion: one employs
means-ends analysis in the attempt to figure out how information might
be manipulated to get from the assumed input to the assumed target output.

Neuroscience offers a different opportunity for decomposition, and one
that has already proven richly rewarding in the approximately quarter century
that it has been applied. The difference lies in the tools for developing
the task decomposition. Instead of relying on one's abilities in means-end
analysis to come up with a decomposition, neuroscience offers a variety
of empirical tools: neural-anatomical studies of cell types and patterns
of connectivity, recordings from individual neurons, studies of deficits
when specific neural structures are destroyed, and most recently imaging
of activity patterns in actively functioning brains. None of these alone
offers a simple and foolproof route to determining how the brain decomposes
the task of visual perception, and where the processes are localized, but
together they offer a set of tools for constraining the decomposition and
localization.

Figure 2. From Zeki (1976).

It has long been recognized that the key cortical areas for initiating
visual processing lie in the rear of the brain in the occipital lobe, more
specifically, in Brodmann's area 17 or V1. Neuroanatomical studies in the
1960s showed five different pathways from V1 into Brodmann's area 18, or
prestriate cortex: to V2 (2), V3 (3) , superior temporal sulcus (5), V4
(3), and V4a (4) (Craig, 1969). (Figure 2) This suggested the possibility
of different types of processing along each of these pathways. By providing
different visual stimuli and recording from cells in each of these areas,
researchers such as Zeki (1976) discovered that cells in different regions
of prestriate cortex responded to different types of information--e.g.,
those in V2 to binocular disparity, those in V4 to specific colors, while
those on the superior temporal sulcus responded to motion.

Figure 3. From Mishkin et al. (1983).

One of the most heralded findings of modern neuroscience, and we would
suggest most surprising from the point of view of attempts at top-down
decomposition of cognitive processing, arose from tracing the processing
beyond the prestriate cortex areas. Mishkin, Ungerleider, and Marko (1983),
relying largely on lesion studies in monkeys (the first study in this series
was Pohl, 1973), differentiated two main routes for processing visual information.
(Figure 3) From prestriate cortex, one route proceeds ventrally into posterior
temporal cortex along the inferior longitudinal fascilus to areas TEO and
TE. Based on the fact that lesions in the posterior temporal area result
in loss of pattern discrimination and that lesions in TE in particular
result in failure to recognize previously presented objects, Mishkin et
al. assigned to this pathway the task of analyzing "the physical properties
of a visual object (such as its size, color, texture and shape)" (p.
20). Since there are neural pathways between TE and limbic structures in
the temporal lobe and ventral portions of the frontal cortex, Mishkin et
al. further claim that this area figures in assigning meaning to visual
stimuli. One feature of neurons in the ventral pathway is that as one proceeds
further, the cells have increasingly large receptive fields, suggesting
that they become responsible for recognizing objects independently of where
they appear in the visual field. Performing a location independent identification,
they contend, requires discarding any location information:

It is significant that by virtue of the extremely large visual receptive
fields of inferior temporal neurons this area seems to provide the neural
basis for the phenomenon of stimulus equivalence across retinal translation;
i.e. the ability to recognize a stimulus as the same, regardless of its
position in the visual field. But a necessary consequence of this mechanism
for stimulus equivalence is that within the occipitotemporal pathway itself
there is a loss of information about the visual location of the object
being identified (p. 21).

The other route proceeds from prestriate cortex dorsally into the posterior
parietal cortex. Lesions in the posterior parietal cortex in monkeys result
in failure to be able to select a response location on the basis of a visual
landmark, suggesting that this pathway figures in "perception of spatial
relations among objects, and not in their intrinsic qualities" (p.
21). Further studies revealed that parietal area PG is responsive to tactile
input as well as visual, suggesting that it processes polysensory spatial
information. Unlike the ventral pathway, this parietal pathway is limited
to input from the contralateral part of the visual field. Moreover, whereas
the temporal pathway is primarily sensitive to foveal areas, the parietal
pathway is equally sensitive to foveal and peripheral areas.

The two systems differentiated by Mishkin et al. quickly became known
popularly as the "What" and "Where" systems. Further
research revealed that the two pathways diverge even before V1 and employ
different cells in V1. The retina projects to V1 via the Lateral Geniculate
Nucleus (LGN) and even in the LGN there are two steams, a parvocellular
(P) and a magnocellular (M) stream. [2] These two streams
receive projections from different retinal ganglia and project into different
cells in V1, with the P pathway then projecting into inferotemporal cortex
(the What system) and the M pathway projecting into parietal cortex (Where
system) (Maunsell and Newsome, 1987). The original work differentiating
the two systems used lesion and deficit studies, but it was complemented
by blood flow tracer studies with radioactively labeled deoxyglucose (Macko,
Kennedy, Sokoloff, and Mishkin, 1981). These revealed one pathway from
V1 to V2 (OB in Figure 3), to V3 and V4 (both in ventral OA in Figure 3),
and from V4 to both TEO and TE, and another that begins with projections
from both V1 and V2 to MT (mostly within dorsal OA), and from there to
four areas in the upper superior temporal and intraparietal sulci (which
is within PG).

The advent of PET provided a way of demonstrating functionally distinct
pathways in normal humans, and Haxby, Grady, Horwitz, Ungerleider, Mishkin,
Carson, Herscovitch, Schapiro, and Rapoport (1991) proceeded to take advantage
of this new technology. In PET studies, blood flow is measured (indirectly)
in different tasks; the resulting patterns produced for each task can then
be subtracted from one another so as to reveal areas of the brain that
are active in one but not the other task. Haxby et al. used a face matching
and a dot matching task as the target tasks, and subtracted from the resulting
images the images produced in a sensorimotor control task (this is the
same substraction methodology mentioned above in the context of reaction
time studies). Their major finding, in accord with the What/Where distinction,
was that

The face-matching task caused rCBF [regional cerebral blood flow] increases
in a broad region extending from the occipital pole to posterior temporal
cortex in all subjects. The dot-matching tasks caused rCBF increases in
superior parietal cortex in 8 of 11 subjects and rCBF increases in superior
parietal cortex in 10 of 11 subjects. The lateral occipital area activated
during spatial visual processing overlapped with the areas activated during
face matching but tended to be closer to the occipitoparietal border and
did not include the more anterior and superior occipitotemporal region
(1623).

While the What/Where distinction arose first in neuroscience, it has
subsequently influenced research in computational and behavioral areas
of cognitive science. In computational modeling, it helped foster inquiry
into modular networks by connectionists. Early connectionist models tended
to be layered networks in which each unit in one layer was connected to
every unit in preceding and succeeding layers. In modular networks, different
tasks are performed by different subnetworks, thus introducing a task decomposition.
One sort of modular design for neural networks involves having several
discrete networks receive input from the same source, but with different
networks performing different tasks with this input. Each network will
respond to every input, but either the networks can be trained to perform
different tasks on the input by having separate response units, or a gating
network can regulate which network will have access to the output units
for a given task, and consequently learn from its performance on that task.
In a first demonstration of the potential of modular networks to solve
cognitive tasks, Rueckl, Cove, and Kosslyn trained a network with separate
sets of hidden and output units to identify the object in one subnetwork
and specify where it was in another. The input was a 5x5 "retina"
in which any one of nine different object patterns could occupy one of
the nine different 3x3 matrices within the retina (in Figure 4, for example,
a "T" is in the central matrix). They showed that the network
learned much more quickly if one used separate sets of the hidden units
and allowed one set to feed each set of output units than if there were
a common set of hidden units.

Figure 4

Jacobs, Jordan, and Barto (1991) extended the demonstration to show
that a network could learn to subdivide these tasks between subnetworks.
They employed a gating network, which learned to control which subnetwork
had access to the output units and received the learning signal (Figure
5).

Figure 5.

On a given task, the network was to specify either the location or the
name of the object. The subnetworks each had a different configuration
(one had no hidden layer, one had 18 hidden units, and the third had 36
hidden units). Through backpropagation learning in both the modular subnetworks
and the gating network, the system learned in 50 epochs to divide the tasks
so that the network with no hidden units performed the where task
and the network with 36 hidden units performed the what task. This
is, in fact, an optimal distribution since the where task is linearly
separable and thus efficiently handled by a network without hidden units,
whereas the what task requires hidden units. As a comparison, Jacobs et
al. attempted to train a single network to handle both tasks, but such
a network never learned as well, and was subject to catastrophic interference
in which learning one task interfered with the ability to perform the other
task. What this suggests is that the decomposition employed by the brain
may be computationally efficient.

The What/Where distinction has also begun to have an impact in psychology.
One of the first invocations of it was by Neisser (1989), who invoked it
to suggest how Gibsonian ecological psychology and computational cognitive
psychology might be brought together. He contended that Gibson was most
concerned with perceiving aspects of the environmental layout and that
this might be determined directly. Jacobs et al.'s demonstration that where
decisions require only a two-layer network and hence no intermediate steps
might provide some sense to this directness claim, although probably not
the one Gibson and Neisser were concerned with. The what system, on the
other hand, may have to carry out more complex operations, making a decomposition
of this task into multiple inferential steps more plausible. Until recently,
however, there was little purely psychological evidence for the What/Where
distinction. Indeed, evidence in the speed of processing literature suggested
that there was only one factor that accounted for a subject's speed of
processing on a wide variety of cognitive tasks. Hale and Jansen (1994),
for example, showed that on a variety of cognitive tasks (choice reaction
time, letter classification, visual search, abstract matching, line-length
discrimination, mental rotation, and mental folding), an individual subject's
reaction times were a linear function of the mean reaction times of an
entire group. Thus, a given individual might have reaction times slower
or faster than the whole group, but this speed up or slow down would carry
across all tasks studied. This provided evidence of a single "global
processing-time coefficient [that] characterizes the speed of all of an
individual's basic cognitive processes" (388). This is not what one
would expect if there were different cognitive systems performing these
tasks. However, the tasks in Hale and Jansen's study were not chosen so
as to differentiate spatial location tasks from object identification tasks.
More recently, Hale and her colleagues (Hale, Chen, Myerson, & Simon,
1996) have devised tasks to differentially emphasize spatial location determination
and object identification procedures. In this new study, they have found
subjects with differential advantage either on what tasks or where tasks.

Our major contention is that in this case, information drawn from the
brain has played a major role in developing the task decomposition. The
what/where distinction did not arise from top-down theoretical considerations,
but from differentiating neural components that did different tasks. Once
identified, though, the decomposition can be employed in all the relevant
disciplines. Thus, we have seen how it has fed back into computational
modeling and into psychological experimentation.

Unfortunately, we must note that while the decomposition and localization
of visual processing areas, and the spin offs into computational modeling
and experimental psychology, that we have discussed so far present an intellectually
coherent and satisfying story, nature may turn out to be more complicated.
The details of the neuroanatomy of the visual cortex of the macaque indicate
far more communication between processing channels than the two-pathway
model acknowledges. Van Essen and his colleagues (Felleman and van Essen,
1991; van Essen and DeYoe, 1995), using a

Fig. 6: From van Essen and Gallant (1994), p. 2.

variety of techniques to flatten and map the macaque visual cortex,
identify 32 distinct areas (Figure 6). [3] They have
also shown that these areas on average receive 10 inputs and send 10
outputsFigure 7. From van Essen and Gallant (1994), p. 3.

to other areas so that overall nearly one third of the possible corticocortical
connections are realized. (Some of these pathways may be relatively weak,
as they are "only faintly detectable using the most sensitive available
technique", van Essen and DeYoe, 1995, p. 386). Moreover, these connections
tend to be reciprocal. [4] Cross talk between the different
pathways begins already in V1. The M stream cells in V1 receive inputs
from both the M and P steam, although inputs through the M stream predominate.
Moreover, axons project across the 4C/4C boundary (see Figure 7), and there
are further such projections on up the processing pathways. From anatomical
studies alone one cannot determine the functional significance of different
projections; more direct functional evidence can be gathered by selectively
inactivating a particular input stream and observing the effects on cells
downstream. The fact that even after inactivating P-layer cells in LGN,
cells in the blob area of V1 can still be driven indicates that they are
receiving functional inputs from the M and K streams (Nealey, Ferrera,
and Maunsell, 1991). Both the blob and the interblob areas (the latter
are primarily driven by P cells in LGN) of V1 project to distinct parts
of V4 and the posterior inferior temporal cortex, although V4 also receives
input from the cells in V3, part of the M stream.

If the contentions of van Essen and his colleagues are correct, then
the mechanistic account of visual processing will turn out to be more complex
than expected. This, however, is the norm in science. The first accounts
of biochemical processes such a fermentation and oxidation proposed moderately
simple, linear chains of reactions. Subsequent research resulted in the
discovery of significant nonlinearities. These, however, did not require
repudiating the attempt to decompose biological processes, but the development
of more complex accounts of what the contributing processes are and how
they are integrated (Bechtel, 1986, Bechtel and Richardson, 1993). We suspect
that much the same is now happening in understanding of visual processing.
As research continues, decomposition and localization will continue to
interact with each other. In some cases decompositions developed from focusing
on the task and carrying out means-end reasoning will provide the basis
for identifying physical components and their functions. In other cases,
the discovery of the components may lead in the process of developing the
decomposition.

b. Decomposition and Localization of Attentional Mechanisms

Attention has been a topic of consideration in cognitive theorizing
since the cognitive revolution, but research here too has recently benefited
from brain based investigation. Our discussion will focus on three models
of attention. The first was introduced by Broadbent (1958) and others primarily
on the basis of behavioral evidence that suggested the existence of a limited
cognitive resource, or 'bottle-neck,' that stimulus information competes
for, somewhere between perception and motoric response. The second model
was introduced by Michael Posner on the basis of behavioral and neural
evidence. According to the second model there are three attentional systems,
a visuo-spatial, posterior network, a non-modality-specific, top-down,
anterior network, and a vigilance network. The posterior system is conceptualized
as a 'spotlight' that illuminates different locations in the visual field.
The third model is Desimone and Duncan's attentional template, biased competition
model, in which different processing streams compete for the control of
overt behavioral response, and receive biases from an attentional template
according to their relevance to the demands of a task.

Attentional effects are measured behaviorally by comparing reaction
times and error scores on tasks under different conditions. Differences
in performance are taken to indicate that under certain conditions human
information processing resources are more taxed than under other conditions.
Thus, in one type of task, the visual search task, two conditions are compared.
In both conditions, there is an array of objects or shapes that must be
searched in order to discover some target that is Figure 7.

pre-specified in terms of a unique feature or set of features. In the
first condition, the target is pre-specified in terms of an obviously distinguishing
feature. For example, the array may consist of a set of green shapes and
one red shape, and the subject may be asked to identify the red shape (Figure
7). Reaction time in this condition does not increase with the number of
green shapes, or distractors, in the array. In the second condition, the
target is pre-specified in terms of a non-obvious feature or set of features,
like a conjunction of features. For example, the array may consist of different
shapes of various colors, and the subject may be asked to identify the
yellow triangle . Figure 8.

(Figure 8). Relative to this array, this conjunction of properties is
non- obvious. The subject must engage in controlled, or attention-demanding
processing, to discover the target. It is for this reason that reaction
time in this condition increases with the number of distractors, i.e.,
colored shapes, in the array.

The limited capacity model explains the different performance in these
two conditions in the following way. In the first condition, a relatively
unlimited, pre-attentive perceptual capacity scans the array in parallel,
and selects the target automatically for further processing by the limited
attentional resource, and eventually for motoric response. Because the
array is scanned in parallel by a relatively unlimited, pre-attentive cognitive
resource, the number of distractors has little or no effect on reaction
time. In the second condition, the limited attentional resource must be
used in a controlled, serial search for the target. Each item in the array
must be selected for processing by the limited attentional resource, in
sequence, until the target is found. It is for this reason that reaction
time increases with the number of items to be processed.

The understanding of such results in terms of selection by a limited
attentional resource suggests a very definite research program. Attention
is functionally analyzed as a processing 'bottle-neck' or limited capacity
resource that selects from competing stimulus information represented automatically
and in parallel with pre-attentive perceptual resources. Such a model raises
the following questions. When in the processing of perceptual input, and
on what basis, is information selected for processing by the limited attentional
resource? What type of processing, e.g., semantic, formal, etc., is the
limited attentional resource required for? What are the relationships and
differences between controlled and automatic processing? These functional
questions about the types of processing that distinguish the limited attentional
resource from less limited pre-attentive resources suggest corresponding
questions about the localization of the relevant cognitive functions. Where
in the brain are limited attentional resources found? Where in the brain
does pre-attentive, automatic, parallel processing occur? How does selection
among information competing for attention occur in the brain?

Michael Posner is one of the first cognitive psychologists to bring
brain research to bear on the study of attention and, as we shall see,
in his hands brain studies do not answer these questions, but lead to their
replacement. His work concentrates on visuo-spatial attention, which he
defines in terms of the following experimental paradigm. Posner studies
covert attentional effects in visual processing, that is, attentional effects
that are not facilitated by overt eye movements. A typical experiment involves
three locations on a screen facing an alert subject (Figure 9). In the
middle of the screen is a fixation

Figure 9. From Posner and Raichle (1994), p. 156.

point on which the subject must focus her vision. There is a square
on each side of the fixation point, separated by some angle of eccentricity.
In a typical experiment, the subject first fixates on the fixation point.
Next, the subject is cued by a luminance increment in one of the boxes.
The subject is trained not to respond to this luminance increment with
a saccade or any other motoric response. Finally, a target, like an asterisk,
appears in one of the boxes. The subject's task is to respond to the target,
with a key press for example, as quickly as possible. Posner has obtained
the following results with this experimental paradigm. If the target appears
in the cued location within 100 msec after the cue, then the subject's
processing of that information is facilitated in comparison with the condition
where the target appears at an uncued location. This facilitation is measured
in terms of reaction time. Thus, the subject takes longer to respond when
the target appears at an uncued location than when it appears at the cued
location.

Prima facie such results can be accounted for using the classical model
of limited attentional resources. Visual space is represented by an automatic,
pre-attentive, parallel cognitive resource. The representations of the
different locations in this space compete for further processing by the
limited attentional resource. The cuing of a particular location in visual
space leads to the selection of the representation of that location for
further processing. Assuming that a response to the target requires processing
by the limited attentional resource, it is not surprising that responses
to targets appearing in cued locations are facilitated. The cue has already
led to the selection of that location for further, attentional processing,
so when the target appears there it can be processed more rapidly than
when it appears at a location that has not been cued, and is not already
being processed by the limited attentional resource.

Despite its prima facie compatibility with the classical cognitive analysis
of attention in terms of selection for processing by a limited resource,
Posner develops a different and more complicated model of visuo-spatial
attention based on more fine-grained evidence obtained from experiments
with subjects with brain lesions and from PET studies. This work constitutes
an early example of neural evidence modifying and constraining the development
of a cognitive model. The PET studies show that a massively distributed
network of brain areas involving parietal cortex, the midbrain, and the
thalamus is active during performance of the covert, visuo-spatial attention
task (Posner, et al. 1994, p.221). And the lesion studies show that different
deficits in the performance of this task result from lesions in different
areas of this network.(ibid.) Posner uses such evidence to argue for the
claim that visuo-spatial attention is realized by elementary cognitive
operations, each of which is localized in a specific neural structure (Posner
1992, p.12). Because parietal lesions only affect the capacity to shift
attention from the ipsi- to the contralesional side, and not vice versa,
Posner argues that the posterior parietal lobe must be responsible for
disengaging attention to a location in the ipsilesional visual hemifield.
Because damage to midbrain structures (especially the superior colliculus)
affects the capacity to shift attention in both directions, Posner argued
that these midbrain structures must be responsible for the actual move
from one attended location to another. Analogous lesion evidence and similar
argumentation leads Posner to conclude that parts of the thalamus (the
pulvinar) are responsible for an engage operation, whereby attention to
a new location is engaged after a shift from an old location (ibid.). (Figure
10)
Fig. 10. From Posner and Raichle (1994), p. 168

It is clear that Posner's fine-grained analysis of the role of specific
neural structures in the performance of the covert, visuo-spatial attention
task constitutes a radical reconceptualization of the cognitive role of
attention. The metaphor of the limited processing bottle-neck is replaced
by a "spotlight" metaphor (Posner, et al. 1994, p.224). These
metaphors differ along several interesting dimensions. Posner's work suggests
that attentional deficits are not deficits in types of processing, but
rather deficits in efficiency of processing. Victims of lesions with attentional
deficits still have the capacity to process information from every sensory
modality, as well as semantic information, and high-level information about
visual form (Posner, et al. 1984, p.1873). Their problem is that they cannot
enhance this processing to the requisite degree when it is required for
the purposes of some task. The spotlight metaphor is a more appropriate
way of thinking about these deficits. It is not that some information cannot
make it into a limited capacity bottle-neck. Rather, the system responsible
for enhancing processing in primary sensory areas, as demanded by some
task, is disabled with respect to one of these areas. The spotlight has
trouble 'reaching' some areas of visual space. Along with this reconceptualization
comes a cognitive/functional distinction that makes little sense on the
traditional processing bottle-neck model of attention. When we reconceptualize
attention as a spotlight, it makes sense to distinguish between the "site"
and the "source" of attention (Corbetta, et al. 1991, p.2384)
The site of attention is the neural area whose activity is enhanced for
the purposes of some task. The source of attention is the neural area that
effects this enhancement. Thus, where the traditional bottle-neck model
of attention makes a distinction between two types of processing, i.e.,
pre-attentive vs. attentive, that take place at different sites, the spotlight
model of attention sees attention as the enhancement of processing at various
pre-attentive sites, caused by sources of enhancement like the posterior
attention network.

The site/source distinction emerges quite naturally in the neurally
informed study of attention. When one studies brain anatomy, no obvious
candidates suggest themselves as locations of the processing bottle-neck
that defines attention in the classical model. All one sees is massively
parallel, distributed processing. For the neurally-informed student of
attention, attention is not a separate, limited-capacity processing module.
Rather, it is the activity of coordinating parallel processing streams
through selective enhancement. As Allport argues, the only limited resource
that parallel, pre-attentive processing streams compete for is the control
of behavior (Allport 1989, pp.648-652). Coherent, coordinated behavior
inevitably involves selectivity. Only a small number of overt actions can
be performed at the same time. For example, one cannot both raise one's
arm and lower it at the same time. The temptation to treat attention as
a limited-capacity processing module is an artifact of the fact that the
tasks used to measure attention involve coordinated, overt responses, and
thus the inevitable selection of some processing streams over others. Thus,
to speak of competition for a limited processing resource is less an explanation
of attention than a description of what needs to be explained: the fact
that in the type of coordinated activity that is required in tasks used
to measure attention, only one processing stream can control overt behavior
at a time (ibid., p.640). The 'spotlight' metaphor and the site/source
distinction, on the other hand, suggest a functional analysis of attention
that is much more explanatory, relative to facts about the brain.

Despite the reconceptualization of attention as a spotlight, many of
the questions suggested by the bottle-neck model remain. It is still not
obvious how selection works. On what basis does the spotlight select a
specific neural area to enhance? Posner's work suggests that low level
cues, like luminance changes at specific locations in the visual field,
may lead to the enhancement of relevant neural areas. This seems to be
an example of "stimulus-driven attentional capture" (see Yantis
1993). In both Posner's covert attention shift task, and the 'obvious'
condition of the visual search task, attention is the product of a pre-attentive
process. However, performance on the 'non-obvious' condition of the visual
search task suggests a very different attentional effect. Here, the actual
search seems to demand attention. Attention is part of the controlled,
sequential processing that is necessary to locate the target. Posner tries
to localize the division between the kind of attention that is automatically
captured by luminance changes at locations in the visual field, and the
kind of controlled attentional processing required for serial search, in
an anatomical division between what he calls the anterior and posterior
attention networks. As we have seen, the posterior attention network explains
the capture of attention by luminance changes at specific locations in
the visual field. Posner argues that the anterior attention network, and
especially a neural area called the anterior cingulate, is responsible
for the kind of controlled, attention-demanding processing that we see
in tasks involving search for non-obvious targets. He hypothesizes that
the anterior attention network (Figure 11) is where non-modality-specific,
linguistically conveyed information is brought to bear on the control of
attention (Posner, et al. 1994, p.219). Such information permits the controlled,
attention-demanding search for targets which are not distinguished from
distractors by obvious, modality-specific cues.

Figure 11. From Posner and Raichle (1994). p. 173.

However matters are not quite so straightforward. In a PET study, Corbetta,
et al., examined the neural activation that accompanies performance on
a visual search task involving two frames representing an array of moving
objects. The subjects were asked to report if the two frames were the same
or different with respect to certain visual features, like color, shape
and speed. There were two conditions. In the 'divided-attention' condition
subjects were asked to report if they noticed a difference in any of the
object features, color, shape, or speed. In the 'selective-attention' condition,
subjects were asked to report if they noticed a difference in a specific
object feature, either color, shape, or speed. These two conditions yielded
dramatic differences in the pattern of neural activation. Where the selective-attention
condition involved primarily the enhancement of early visual areas responsible
for the detection of the relevant feature, the more attention-demanding
divided-attention condition involved additional activation of the anterior
cingulate (Corbetta, et. al 1991). This seems to confirm that the anterior
cingulate is involved in controlled, attention-demanding processing. However
the experiment also seems to suggest that Posner's characterization of
the difference between anterior attentional function and posterior attentional
function is misleading, for in both the divided- attention and the selective-attention
conditions, non-modality-specific, linguistic instructions play a role
in the control of attention. In the selective-attention condition, which
does not result in activation of the anterior cingulate, the instruction
to detect changes in color as opposed to shape or motion, for example,
activates the appropriate automatic selection for attention.

Corbetta et. al.'s results are consistent with increasing evidence from
behavioral studies of attention that there are very few examples of stimulus
information that can capture attention 'from the bottom up'. In other words,
it is hard to specify what captures attention in a task- neutral way. Many
high level features, like word form or even semantic properties, can capture
attention automatically (Duncan 1980, p.291). Conversely, attention to
supposedly compelling low level cues, like luminance changes, can be successfully
and automatically inhibited if the task requires attention to properties
that vary independently of such low level cues (Yantis 1993). Such evidence
leads Steven Yantis to argue that Posner's studies are not examples of
bottom-up attentional capture by luminance changes, since the requirements
of the task, i.e., reaction to target onset, necessitate the detection
of luminance changes (ibid., p.159). Given this argument, Posner's division
of attention into a posterior system that is driven by visual stimuli and
an anterior system that is driven by top-down, task-specific information
seems ill-motivated.

If attentional capture by luminance increments at locations in the visual
field is just a special case of automatic attentional capture by stimulus
information that is most relevant to successful performance on the specific
task that the subject is engaged in, then it may be better to think of
such automatic attentional capture as a general phenomenon that can affect
many different processing dimensions, rather than as a specific attentional
system that scans visual space for inherently salient cues, like a spotlight.
Such a reconceptualization still retains many features of the spotlight
model. It still makes sense to make a distinction between the site and
the source of attentional enhancement of processing. The difference is
that there are many possible sites of such attentional enhancement besides
the processing centers that represent visual space, and there may be correspondingly
many sources of such attentional enhancement. Something like this model
is suggested by Desimone and Duncan (1995, pp.199-200). They argue that
attentional selection involves biased competition among different processing
streams for the control of behavioral response. The competitive bias comes
from an "attentional template" that is formed in conformity with
task demands. In this model, the attentional template is the source of
attentional enhancement, and visual areas that meet the requirements of
the templates are the sites of attentional enhancement.

Desimone and Duncan's model has the advantage of offering both an explanation
of automatic attentional capture by task-relevant features, and an explanation
of why certain tasks require controlled, attention-demanding processing:
when targets and distractors are not distinct along some obvious perceptual
dimension, both representations of targets and of distractors get an equivalent
competitive bias (ibid.). However, this model raises many new questions.
Where exactly are attentional templates located? Are the attentional templates
appropriate for different tasks located in distinct brain areas, or in
the same brain area? How exactly is the weak competitive bias propagated
from attentional templates to sensory processing areas? How are instructions
translated into attentional templates? When targets and distractors are
insufficiently distinct to yield automatic attentional capture, how and
where does controlled, attention- demanding processing take place, and
what is the role of templates in such processing? If attentional templates
play no role in such controlled processing, then how do instructions yield
appropriate sequences of controlled, attention-demanding processing?

Thus, there have been at least three cognitive/functional conceptualizations
of attention, and both neural and cognitive psychological evidence have
contributed to their articulation. Based primarily on behavioral evidence,
attention was first conceptualized as a processing bottle-neck located
somewhere between perception and motor response. Neural evidence threw
doubt on this conceptualization. The brain's massively parallel processing
makes the existence of such a bottle-neck unlikely. This led some to reconceptualize
the limited resource that stimulus information seems to compete for as
the control of overt behavioral response, and the selectivity that adjudicates
such competition as the result of enhancement and amplification of specific
processing sites. The sources of such enhancement and amplification were
thought to be multiple. Some involved top-down task-specific information,
while others seemed to involve low-level visuo-spatial cues. Posner identified
these sources of attentional enhancement as the anterior and posterior
attentional networks, respectively. However, behavioral evidence suggests
that Posner's division is ill-motivated. Most, if not all, automatic, attentional
capture, by supposedly obvious visual and sensory cues, is determined by
top-down influences, i.e., by task-specific information. Thus, the distinction
between a visual-cue based posterior attentional network, and a top-down
task-specific anterior attentional network seems spurious. However, the
distinction between automatic attentional capture, and target detection
that requires controlled, attentional processing still seems robust. It
is reasonable to suggest that the anterior attentional network, and especially
the anterior cingulate, is crucial to the latter, while the former is performed
by a diversity of attentional systems associated with relevant sensory
areas, in something like the way proposed by Desimone and Duncan's biased
competition, attentional template theory.

4. The Interactive Approach and the Dynamical Systems Challenge

Our goal has been to illustrate the interactive processes of functional
decomposition and structural localization which is guiding the development
of mechanistic models of cognitive phenomena in cognitive neuroscience.
In this last section, though, we return briefly to the opposition to such
research. We noted at the outset that there is a long tradition of opposition
to the decomposition and localization of cognitive function. Flourens provided
a strong opponent to Gall. Motivated largely by Cartesian considerations,
but also influenced by lesion experiments which he performed on pigeon
brains, Flourens argued that there was no specialization of function in
the cerebral cortex:

All sensations, all perceptions, and all volition occupy concurrently
the same seat in these organs. The faculty of sensation, perception, and
volition is then essentially one faculty (1824, p. 241)

Lashley's opposition to cerebral localization was also inspired by lesion
work, this time on rats, which showed no deficits to maze running ability
after removal of large portions of the frontal cortex. Lashley (1929).
Lashley interpreted this as evidence both for equipotentiality of frontal
cortex tissue (the ability of any part to carry out functions of the whole
part) and mass action (the degree of performance of a task depends upon
how much of the original part remains). Lashley made exceptions to these
principles for dedicated sensory and motor areas, but applied them to large
parts of the cortex. He also suggested an interpretation of these principles
in terms of Gestalt psychology's principle that higher level activities
depend upon the dynamic organization of the entire cortex.

Lashley's appeal to Gestalt psychology's interest in dynamic organization
links it to today's major opposition to decomposition and localization,
stemming from some advocates of dynamical systems theory. Van Gelder (1995),
for example, associates what he calls homuncularity with other concepts
such as representation, computation, and sequential and cyclic operation,
and contrasts this combined package with the dynamicist's view that seeks
dynamical laws (taking the form, e.g., of differential equations) connecting
different parameters in the overall system. It is reasonably clear that
homuncularity is linked with notions of representation and computation.The
notions of sequential and cyclic operations do not seem so intimately linked,
since mechanists will often abandon sequential operations in their models
when these provide inadequate accounts of the phenomena, and the notion
of cyclic process van Gelder identifies, in which a system runs a complete
cycle before receiving new input, is largely a product of modern computer
design. In decomposing the cognitive system into different component functions,
one identifies different information being supplied to each component,
(hence different representations), and associates with each component a
computation, where computation means some operation on the represented
information. It is true that some dynamical models have a holistic, not
decompositional character to them--variables are identified in the behavior
of the system, and laws are sought relating these variables. But this task
of discovering regularities in behavior is not incompatible with trying
to explain the behavior of a system in terms of its components (Bechtel,
1996). The two tasks are complementary. Moreover, a decomposition and localization
of cognitive function may provide the basis for identifying variables that
figure in an dynamical analysis.

There is a different strand in the dynamicist's criticism, however,
that does directly threaten the sort of decomposition and localization
we have identified in cognitive neuroscience. This strand argues that the
evidence usually adduced for decomposition and localization fails to demonstrate
that there really are different parts doing different functions. van Orden
and Stone (in press) argue that one of the strongest form of evidence presented
for dissociation of function, double dissociation studies in which two
patients show contrasting deficits and preservations of function, fail
to do so. They generalize this criticism to interference studies of the
sort we mentioned earlier and van Orden and Paap (in press b) advance a
similar argument against PET studies. The basis for these objections is
that the evidence that purports to show different functional components
in different neural systems is subject to alternative interpretations in
which a much more integrated system is damaged in different ways. Cohen
et al. (1994), for example, develop a neuronal network model which simulates
the normal and lesioned behavior of the disengage mechanism in Posner's
account of the posterior attention system without including components
whose specific task is to disengage attention. Other neural network studies
such as Hinton and Shallice (1991) likewise show that lesions to interactive
networks can show patterns of deficit resembling those one gets in human
lesions without there being a component specifically responsible for the
lost ability. Moreover, one can get a pattern of activation in a given
area of a system without that component performing a distinctive sub-task
of the overall activity.

This latter challenge from DST is a serious one. One way of viewing
it, though, is as an instance of the more common phenomenon of the underdetermination
of scientific theories. Any amount of empirical evidence is subject to
alternative interpretations. The challenge in science is to develop the
most plausible interpretation of evidence at a time and pursue research
programs that build from that evidence to a more detailed and adequate
account of the phenomenon. The question then becomes not whether the evidence
adduced proves decomposition and localization, but whether it is
part of a progressive research program. Cognitive neuroscience certainly
seems to show all the signs of a being a progressive research program,
and hence this is not the time to abandon it.

Footnotes

[1] A controversial issue, but one which does not
affect the analysis we are offering here, is how one characterizes this
input information. Many have adopted a static and atomized view of the
input. This was severely criticized by Gibson (1974), who argued that the
information that was picked up by a nervous system was far more integrated
and dynamic. Gibson and many inspired by him have pointed out how information
about time to impact with another object is provided through looming, for
example--the speed of the spread of the pattern on the retina.

[2] Yet a third stream through LGN has been distinguished,
a koniocelular (K) stream, but its limited cortical projection and sluggish
response patterns led researchers to discount its significance in transmission
of ascending visual information.

[3] Commenting on the methods of mapping, van Essen
and DeYoe state: "The 32 distinct visual areas shown in this scheme
have been identified on the basis of one or more of the following criteria:
(1) a distinctive pattern of inputs and outputs; (2) a distinctive architecture,
(3) a topographically organized representation of part or all of the contralateral
visual hemifield; and (4) a characteristic functional signature revealed
by physiological recordings or restricted cortical lesions" (p. 386).

[4] When the connections are between lateral areas,
the reciprocal connections tend to be symmetrical--connections in both
directions start in the same layer and terminate in the same layer. This
is not true when the connections are within a processing stream.